- Видео 63
- Просмотров 349 711
Machine Learning at Berkeley
Добавлен 4 фев 2016
Machine Learning at Berkeley empowers passionate students to solve real world data-driven problems through collaboration with companies and internal research. To find out more, check out our website ml.berkeley.edu where you can sign up for our newsletter and apply for our project teams.
BioML Seminar - Rhiju Das on the RNA Folding Problem
Title: Worldwide Competitions and the RNA Folding Problem
Bio:
Rhiju Das is a Professor of Biochemistry at Stanford University School of Medicine. After training in particle physics and cosmology at Harvard, Cambridge, University College London, and Stanford, Dr. Das did postdoctoral research in computational protein folding at the University of Washington with David Baker. On returning to Stanford, Dr. Das set up his lab to focus on computer modeling and design of RNA molecules, which underlie important molecular machines in biology and medicine. As a core part of this research, Dr. Das leads Eterna, an open science platform that crowdsources intractable RNA design problems to 250,000 play...
Bio:
Rhiju Das is a Professor of Biochemistry at Stanford University School of Medicine. After training in particle physics and cosmology at Harvard, Cambridge, University College London, and Stanford, Dr. Das did postdoctoral research in computational protein folding at the University of Washington with David Baker. On returning to Stanford, Dr. Das set up his lab to focus on computer modeling and design of RNA molecules, which underlie important molecular machines in biology and medicine. As a core part of this research, Dr. Das leads Eterna, an open science platform that crowdsources intractable RNA design problems to 250,000 play...
Просмотров: 596
Видео
Deep Learning for Protein Engineering Primer (AlphaFold 2, ProteinMPNN, RFDiffusion)
Просмотров 3 тыс.8 месяцев назад
Listen to Aakarsh Vermani from Machine Learning @ Berkeley talk about applications of machine learning to protein engineering! Description: The talk will give an overview of AlphaFold2, RFDiffusion, and ProteinMPNN, three deep learning models that have been instrumental in the areas of protein structure prediction, protein design, and protein sequence prediction respectively. We’ll talk a litt...
BioML Seminar | Sam Rodriques - Future House
Просмотров 6018 месяцев назад
Inaugural seminar for Spring Series of BioML seminar, hosted by ML@Berkeley (Jan 29th, 2024). Bio: Sam Rodriques is a researcher and entrepreneur with a focus on biotechnology. He founded the Applied Biotechnology Laboratory at the Francis Crick Institute in 2021, aiming to integrate bioengineering and business for medical and biological advancements. Sam's notable inventions include a new nano...
Keerthana Gopalakrishnan & Yao Lu (Google Brain): Large Language Models in Physical Environments
Просмотров 1,6 тыс.2 года назад
Recorded at ML@B General Meeting 10, 11/16/2022.
CS 198-126: Lecture 22 - Multimodal Learning
Просмотров 3,7 тыс.2 года назад
Lecture 22 - Multimodal Learning CS 198-126: Modern Computer Vision and Deep Learning University of California, Berkeley Please visit ml.berkeley.edu/decal/modern-cv to see more information about the course, including slides, assignments, lectures, and course information/background! Instructors: Jake Austin, Arvind Rajaraman, Aryan Jain, Rohan Viswanathan, Ryan Alameddine, & Verona Teo Faculty ...
CS 198-126: Lecture 21 - Generative Audio
Просмотров 1,3 тыс.2 года назад
Lecture 21 - Generative Audio CS 198-126: Modern Computer Vision and Deep Learning University of California, Berkeley Please visit ml.berkeley.edu/decal/modern-cv to see more information about the course, including slides, assignments, lectures, and course information/background! Instructors: Jake Austin, Arvind Rajaraman, Aryan Jain, Rohan Viswanathan, Ryan Alameddine, & Verona Teo Faculty Spo...
CS 198-126: Lecture 20 - Stylizing Images
Просмотров 1,3 тыс.2 года назад
Lecture 20 - Stylizing Images CS 198-126: Modern Computer Vision and Deep Learning University of California, Berkeley Please visit ml.berkeley.edu/decal/modern-cv to see more information about the course, including slides, assignments, lectures, and course information/background! Instructors: Jake Austin, Arvind Rajaraman, Aryan Jain, Rohan Viswanathan, Ryan Alameddine, & Verona Teo Faculty Spo...
CS 198-126: Lecture 19 - Advanced Vision Pretraining
Просмотров 1,5 тыс.2 года назад
Lecture 19 - Advanced Vision Pretraining CS 198-126: Modern Computer Vision and Deep Learning University of California, Berkeley Please visit ml.berkeley.edu/decal/modern-cv to see more information about the course, including slides, assignments, lectures, and course information/background! Instructors: Jake Austin, Arvind Rajaraman, Aryan Jain, Rohan Viswanathan, Ryan Alameddine, & Verona Teo ...
CS 198-126: Lecture 18 - 3-D Vision Survey, Part 2
Просмотров 1,4 тыс.2 года назад
Lecture 18 - 3-D Vision Survey, Part 2 CS 198-126: Modern Computer Vision and Deep Learning University of California, Berkeley Please visit ml.berkeley.edu/decal/modern-cv to see more information about the course, including slides, assignments, lectures, and course information/background! Instructors: Jake Austin, Arvind Rajaraman, Aryan Jain, Rohan Viswanathan, Ryan Alameddine, & Verona Teo Fa...
CS 198-126: Lecture 17 - 3-D Vision Survey, Part 1
Просмотров 1,8 тыс.2 года назад
Lecture 17 - 3-D Vision Survey, Part 1 CS 198-126: Modern Computer Vision and Deep Learning University of California, Berkeley Please visit ml.berkeley.edu/decal/modern-cv to see more information about the course, including slides, assignments, lectures, and course information/background! Instructors: Jake Austin, Arvind Rajaraman, Aryan Jain, Rohan Viswanathan, Ryan Alameddine, & Verona Teo Fa...
CS 198-126: Lecture 16 - Advanced Object Detection and Semantic Segmentation
Просмотров 3,5 тыс.2 года назад
Lecture 16 - Advanced Object Detection and Semantic Segmentation CS 198-126: Modern Computer Vision and Deep Learning University of California, Berkeley Please visit ml.berkeley.edu/decal/modern-cv to see more information about the course, including slides, assignments, lectures, and course information/background! Instructors: Jake Austin, Arvind Rajaraman, Aryan Jain, Rohan Viswanathan, Ryan A...
CS 198-126: Lecture 15 - Vision Transformers
Просмотров 8 тыс.2 года назад
Lecture 15 - Vision Transformers CS 198-126: Modern Computer Vision and Deep Learning University of California, Berkeley Please visit ml.berkeley.edu/decal/modern-cv to see more information about the course, including slides, assignments, lectures, and course information/background! Instructors: Jake Austin, Arvind Rajaraman, Aryan Jain, Rohan Viswanathan, Ryan Alameddine, & Verona Teo Faculty ...
CS 198-126: Lecture 14 - Transformers and Attention
Просмотров 4,7 тыс.2 года назад
Lecture 14 - Transformers and Attention CS 198-126: Modern Computer Vision and Deep Learning University of California, Berkeley Please visit ml.berkeley.edu/decal/modern-cv to see more information about the course, including slides, assignments, lectures, and course information/background! Instructors: Jake Austin, Arvind Rajaraman, Aryan Jain, Rohan Viswanathan, Ryan Alameddine, & Verona Teo F...
CS 198-126: Lecture 13 - Intro to Sequence Modeling
Просмотров 4,3 тыс.2 года назад
Lecture 13 - Intro to Sequence Modeling CS 198-126: Modern Computer Vision and Deep Learning University of California, Berkeley Please visit ml.berkeley.edu/decal/modern-cv to see more information about the course, including slides, assignments, lectures, and course information/background! Instructors: Jake Austin, Arvind Rajaraman, Aryan Jain, Rohan Viswanathan, Ryan Alameddine, & Verona Teo F...
CS 198-126: Lecture 12 - Diffusion Models
Просмотров 84 тыс.2 года назад
Lecture 12 - Diffusion Models CS 198-126: Modern Computer Vision and Deep Learning University of California, Berkeley Please visit ml.berkeley.edu/decal/modern-cv to see more information about the course, including slides, assignments, lectures, and course information/background! Instructors: Jake Austin, Arvind Rajaraman, Aryan Jain, Rohan Viswanathan, Ryan Alameddine, & Verona Teo Faculty Spo...
CS 198-126: Lecture 11 - Advanced GANs
Просмотров 2,4 тыс.2 года назад
CS 198-126: Lecture 11 - Advanced GANs
CS 198-126: Lecture 9 - Autoencoders, VAEs, Generative Modeling
Просмотров 8 тыс.2 года назад
CS 198-126: Lecture 9 - Autoencoders, VAEs, Generative Modeling
CS 198-126: Lecture 8 - Semantic Segmentation
Просмотров 5 тыс.2 года назад
CS 198-126: Lecture 8 - Semantic Segmentation
CS 198-126: Lecture 7 - Object Detection
Просмотров 5 тыс.2 года назад
CS 198-126: Lecture 7 - Object Detection
CS 198-126: Lecture 6 - Advanced Computer Vision Architectures
Просмотров 6 тыс.2 года назад
CS 198-126: Lecture 6 - Advanced Computer Vision Architectures
CS 198-126: Lecture 5 - Intro to Computer Vision
Просмотров 8 тыс.2 года назад
CS 198-126: Lecture 5 - Intro to Computer Vision
CS 198-126: Lecture 4 - Intro to Pretraining and Augmentations
Просмотров 5 тыс.2 года назад
CS 198-126: Lecture 4 - Intro to Pretraining and Augmentations
CS 198-126: Lecture 3 - Intro to Deep Learning, Part 2
Просмотров 5 тыс.2 года назад
CS 198-126: Lecture 3 - Intro to Deep Learning, Part 2
CS 198-126: Lecture 2 - Intro to Deep Learning, Part 1
Просмотров 8 тыс.2 года назад
CS 198-126: Lecture 2 - Intro to Deep Learning, Part 1
CS 198-126: Lecture 1 - Intro to Machine Learning
Просмотров 33 тыс.2 года назад
CS 198-126: Lecture 1 - Intro to Machine Learning
Sp18 ML@B Workshop Series #4: Meta Learning
Просмотров 3,9 тыс.6 лет назад
Sp18 ML@B Workshop Series #4: Meta Learning
Machine Learning Decal Spring 2018 Lecture 6: SVMs & Machine Learning Good Practices
Просмотров 9976 лет назад
Machine Learning Decal Spring 2018 Lecture 6: SVMs & Machine Learning Good Practices
Sp18 ML@B Workshop Series #3: Intro to PyTorch
Просмотров 7836 лет назад
Sp18 ML@B Workshop Series #3: Intro to PyTorch
After 8 years...
I am new to the filed of Biology but not to ML and this is the most enlightening 1h 30 m minutes I had in quite a while
Excellent presentation
came here because of gemini
Thanks.
charlatan
Howdy from Oakland. Lousy audio for a top notch institution recording a technology video.
Cool!
when a machine is better than u at Minecraft😭😭😂🤣
Hey. Thanks for the content! Where can I access to other lectures related to chapters 16 to 20 of the book?
Moves too human. Ai brains use snappy movement because it's more efficient at getting data.
Soo clear
hi. can you post remaining list of lectures, thanks
Can we download this? Are you still updating? Is there a similar project we can download? I want to watch an ai master Minecraft
Is the code public?
Nice explanation.
;)
Thanks for this videos
Good video.
I couldn't find anything on the website about this project. I am also interested in what architecture/algorithm was used since real time style transfer is computationally intensive.
Beautiful 😍..... thank you Soo much for uploading this!!! This helped me alot! Although I was really hoping that you'll upload all the lectures of this series it's only Upto ch15 for now.. but anyways... Have a beautiful year ahead guys! ^_^
Not a so beautiful year :/
lol of curse its a fake and there is no neural network playing Minecraft lol who is the idiot that created it
Lecture starts at 1:54
second part starts at 56:10
Why do you make these 2 minute videos of garbage
Awesome, very clear. Thanks!
Is there way to get copies of the slides? thanks
So now what's its doing two years later?
Great Talk! Crystal clear. Appreciate for uploading.
So fake 😂
So now what's its doing a year later?
Can we take a look at a baby for starters... I walk up and slap a baby, it's prebuilt to "Feel" pain and there's an innate response. We all have them, those pre-built responses, then there's frued, which in the case of AI is a project with errors like crazy, which in a nutshell is us humans, an illogical existence supported by a semi logical framework. No wonder we get headaches...
I guess I'll never live to see the robocalypse :/
Amazing
ITS THINKING HOLY SHIT
This looks completely fake to me. I've seen tons of videos of AI-controlled players playing Minecraft for the first time, and this doesn't resemble any of them. The movements look like they were made from a human player with a mouse. A real AI wouldn't know how to move for the first few minutes, and it'd take even longer to learn how to hit things. Most AI don't even know that they can move sideways, only forward(some can find out they can move backwards). A real AI also wouldn't see the world around it the same way a human would, because it processes things around it using data, not vision, so that debunks the part of the video where it was looking into the spring. It wouldn't just wait there looking into it, because the spring wouldn't even exist to the AI, at least it wouldn't catch its attention. It just looks like this person wanted to pretend they had an AI playing Minecraft, recorded a video of themself walking around very noob-like, added an AI algorithm to the top left of the screen to sell it, and get tons of views. Don't believe this is what AI is like, people. I see tons of comments saying how scary AI is, but it's not the way this person depicted at all.
1:19 When the rabbit attempts to kill itself
Can you please add the missing chapters in the Playlist? Amazing lectures btw.
just leave that on a pc for 4 or 5 years and see what happens would be interesting with ever minecraft mod installed
At 13:50 he predicts the Tesla highway 101 accident scenario - "As I neural network, I took the average and everybody dies"
holy damn
wisam hazm after greeting doctor plz could help me about my equation How extract higher level features from stack auto encoder i need simple explain I need to understand how input 30 and output 28 in stack auto - encoder i mean how stack autoencoder reduce features depends on the hidden layer
god he's insane but i love him
Uhhhhh the AI's learning how to play that game. Am I right people?
"Ah shit, I fell in a puddle. Goddamnit, I dunno how to get out of this, I'm closing this piece of shit game."
make it play darksouls
The comma AI system has a forward looking camera. However the eyes on a human will scan areas in front behind and use wing mirrors. All this information is used when driving. How will the AI learn about this unseen data, also the comma AI can't see the same field of view as a human.
Can you get this thing to play Portal?
Study AI at Berkeley is my dream.
OpenAI is better. Screw Berkeley and their anti white bullshit.
What if you made the ai's only command to kill stuff? And you could say that the ai was going to the water in order to kill the baby rabbit. It was clear ly also trying to use it's fist against the bunny at the beginning.